DYNAMIC SCENE ANALYSIS OF GROUP GAME

团体游戏动态场景分析

基本信息

批准号：
05680308
负责人：
KAWASHIMA Toshio
金额：
$ 0.96万
依托单位：
HOKKAIDO UNIVERSITY
依托单位国家：
日本
项目类别：
Grant-in-Aid for General Scientific Research (C)
财政年份：
1993
资助国家：
日本
起止时间：
1993 至 1994
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/en/grant/KAKENHI-PROJECT-05680308/
关键词：
Dynamic Scene Analysis Qualitative Image Analysis Active-Net X-Y-T Analysis 定性的画像解析

项目摘要

(1). Qualitative analysis of group behaviorGroup behavior of soccer game is analyzed from temporal transition of group relation. Playr groups in the game scene are described as the distribution of by Histogram Backprojection. The distribution maps are smoothed by scale space filtering and a static structure of group hierarchy is computed. We annotated the group behavior in the scene as the temporal transition of group hierarchy.(2). Qualtative analysis of playr's actionThe action of a playr is identified from a sequence of image by examining singular points in X-Y-T space. Image sequence of a playr is binarized and the 3-D skeletal structure is detedted by temporal-continuity-preserving thinning algorithm. The characteristics of the action is annotated based on crossing points and branches in the skeleton. Periodical actions are segmented from the change of the periodicity parameter detected by temporal correlation between images.(3). Playr extraction by Active-NetPlayr extraction method from game scene has been developed. In the algorithm, the Active-Net is applied to Histogram Backprojection images to find the boundary of multi-colored entities. Histogram Backprojection is tuned up to descriminate the colors of uniforms. We have also proposed an automatic parameter adjustment method to emprove the speed to converge, and a net-tering algorthm for exact shape extraction.

（1）。从群体关系的时间过渡中分析了足球比赛的群体行为组行为的定性分析。游戏场景中的Playr组被描述为直方图反向投影的分布。通过比例空间过滤对分布图进行平滑，并计算组层次结构的静态结构。我们将场景中的小组行为注释为组层次结构的时间过渡。（2）。通过检查X-Y-T空间中的单数点，从一系列图像中确定了Playr的动作的质分析。 Playr的图像序列是二进制的，并通过暂时性固定性变薄算法来检测3-D骨骼结构。基于骨骼中的交叉点和分支，对动作的特征进行注释。从图像之间的时间相关性检测到的周期性参数的更改中，定期动作是从图像中所检测到的。（3）。已经开发了从游戏场景中通过Active-NetPlayr提取方法提取Playr。在该算法中，将活动网络应用于直方图反向投影图像，以找到多色实体的边界。直方图进行调整以描述均匀的颜色。我们还提出了一种自动参数调整方法，以使速度融合速度，以及用于精确形状提取的净值algorthm。